The present embodiments relate to medical imaging of three-dimensional (3D) scans. Data representing a volume is rendered for visualization. Due to the many different scan settings and patient variability, renderings for different patients or at different times appear different.
To reduce variability, most existing medical rendering provides a set of static visualization presets for specific workflows or diagnostic contexts. Even with static presets, the rendering may require manual adjustment and may not provide consistent rendered results. Patient and scanner variability may also continue to contribute to inconsistency. Semi-automatic machine learning approaches have been used to provide transfer function design in traditional volume rendering (e.g., ray casting or alpha blending), but may still not sufficiently reduce variability.
Variability is particularly troublesome for physically-based volume rendering, which relies on the physical simulation of light propagation (e.g., unbiased path tracing). Physically-based visualization techniques produce global illumination effects in computer-generated graphics that mimic the real-world interaction of light with various 3D objects or tissues. This results in physically plausible images that are often easier for the human brain to interpret when compared to the more analytical images from traditional rendering. These physically-based visualization techniques are more sensitive to changes in the classification or the interpretation of the underlying medical data. As a result, small changes to the rendering parameters may have a more pronounced impact on the perception and interpretation of 3D structures in the final image as compared to the traditional volume rendering techniques. Obtaining very high quality reproducible images with diagnostic significance is then partially at the user's discretion. While existing techniques for providing visualization presets may help, the resulting rendered images may not have consistent quantitative properties (color, hue, reflectance, etc.) across datasets. Physically-based rendering also takes longer to generate a rendered image, so alteration to reduce variability is time consuming.